AutoNAV: A Python package for simulating UAV navigation in satellite-less environments

Abstract

The majority of current commercial available Unmanned Aerial Vehicles (UAV) depend mainly on satellite signals to estimate their position. Although this is not a problem in outdoor environments, in scenarios where these signals are obstructed, the UAV may be unable to estimate its position, rendering navigation infeasible. Thus, in satellite-less environments alternative methods are required. The AutoNAV package allows the simulation of Unmanned Aerial Vehicles (UAVs) in environments with limited-to-no satellite signal (such as indoor environments), taking advantage of (terrestrial) radio measurements between stationary reference points (anchors) and a UAV to estimate the position of the UAV. The package provides Python implementations of two algorithms for this purpose: Generalized Trust Region Sub-Problem (GTRS) and Weighted Least Squares (WLS). To provide a user-friendly experience and a straightforward comparison process for other researchers, functions that extract the most relevant metrics and plot the ground truth trajectory and the estimated positions by the algorithms are also included. In the provided examples, a scenario of UAV navigation inside a warehouse, e.g., for stock inventory counting, is designed and depicted. Moreover, the package is designed with modularity in mind, enabling researchers to easily implement, compare and integrate their work, fostering a centralized and straightforward approach for analyzing the performance of existing methods.

Keywords

• Generalized Trust Region Sub-problem (GTRS)
• Weighted Least Squares (WLS)
• Unmanned Aerial Vehicle (UAV)
• Navigation
• Indoor